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Autophagy-related gene model as a novel risk factor for schizophrenia.

ABSTRACT: Autophagy, a cellular process where cells degrade and recycle their own components, has garnered attention for its potential role in psychiatric disorders, including schizophrenia (SCZ). This study aimed to construct and validate a new autophagy-related gene (ARG) risk model for SCZ. First, we analyzed differential expressions in the GSE38484 training set, identifying 4,754 differentially expressed genes (DEGs) between SCZ and control groups. Using the Human Autophagy Database (HADb) database, we cataloged 232 ARGs and pinpointed 80 autophagy-related DEGs (AR-DEGs) after intersecting them with DEGs. Subsequent analyses, including metascape gene annotation, pathway and process enrichment, and protein-protein interaction enrichment, were performed on the 80 AR-DEGs to delve deeper into their biological roles and associated molecular pathways. From this, we identified 34 candidate risk AR-DEGs (RAR-DEGs) and honed this list to final RAR-DEGs via a constructed and optimized logistic regression model. These genes include VAMP7, PTEN, WIPI2, PARP1, DNAJB9, SH3GLB1, ATF4, EIF4G1, EGFR, CDKN1A, CFLAR, FAS, BCL2L1 and BNIP3. Using these findings, we crafted a nomogram to predict SCZ risk for individual samples. In summary, our study offers deeper insights into SCZ's molecular pathogenesis and paves the way for innovative approaches in risk prediction, gene-targeted diagnosis, and community-based SCZ treatments.

SUBMITTER: Tan Y

PROVIDER: S-EPMC10864401 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

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Autophagy-related gene model as a novel risk factor for schizophrenia.

Tan Yunfei Y Zhu Junpeng J Hashimoto Kenji K

Translational psychiatry 20240213 1

Autophagy, a cellular process where cells degrade and recycle their own components, has garnered attention for its potential role in psychiatric disorders, including schizophrenia (SCZ). This study aimed to construct and validate a new autophagy-related gene (ARG) risk model for SCZ. First, we analyzed differential expressions in the GSE38484 training set, identifying 4,754 differentially expressed genes (DEGs) between SCZ and control groups. Using the Human Autophagy Database (HADb) database, w ...[more]

PMID: 38351068

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Project description:BackgroundAccumulating evidence implies that autophagy plays a critical role in breast cancer development and progression. It is crucial to screen out autophagy-related encoding genes (ARGs) with prognostic value in breast cancer and reveal their biological properties in the aggressiveness of breast cancer.MethodsUnivariate and multivariate Cox proportional hazards analyses were used to identify a prognostic risk model of ARGs from The Cancer Genome Atlas (TCGA). Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and receiver operating characteristic (ROC) curve analysis were performed to validate the risk model. Western blot and immunohistochemistry (IHC) were conducted to assess the expression of VPS35 (one of ARGs in risk model). CCK8, Colony formation assay, Transwell migration/invasion assays and autophagy flux assay were used to confirm biological function of VPS35 in breast cancer.ResultsIn this study, the prognostic risk model consisting of six ARGs (VPS35, TRIM21, PRKAB2, RUFY4, MAP1LC3A and LARP1) in breast cancer were identified. The risk model was further verified as a novel independent prognostic factor for breast cancer patients. We also clarified that vacuolar protein sorting-associated protein 35 (VPS35), one of ARGs in the risk model, was upregulated in breast cancer samples and cell lines. VPS35 overexpression was correlated with more aggressive phenotype of breast cancer and indicated worse prognosis in both progression-free survival and overall survival analyses. Meanwhile, VPS35 knockdown inhibited breast cancer cell proliferation, migration and invasion, suggesting that VPS35 promoted the progression of breast cancer. VPS35 silence also influenced autophagy process, indicating that VPS35 was essential for autophagy completion.ConclusionTaken together, the six ARGs risk model has a remarkably prognostic value for breast cancer. Among them, VPS35 might exert as a significant oncogenic and prognostic factor for breast cancer and could be a promising autophagy-related therapeutic target in clinical practice.

Project description:The prognosis of colon adenocarcinoma (COAD) remains poor. However, the specific and sensitive biomarkers for diagnosis and prognosis of COAD are absent. Transcription factors (TFs) are involved in many biological processes in cells. As the molecule of the signal pathway of the terminal effectors, TFs play important roles in tumorigenesis and development. A growing body of research suggests that aberrant TFs contribute to the development of COAD, as well as to its clinicopathological features and prognosis. In consequence, a few studies have investigated the relationship between the TF-related risk model and the prognosis of COAD. Therefore, in this article, we hope to develop a prognostic risk model based on TFs to predict the prognosis of patients with COAD. The mRNA transcription data and corresponding clinical data were downloaded from TCGA and GEO. Then, 141 differentially expressed genes, validated by the GEPIA2 database, were identified by differential expression analysis between normal and tumor samples. Univariate, multivariate and Lasso Cox regression analysis were performed to identify seven prognostic genes (E2F3, ETS2, HLF, HSF4, KLF4, MEIS2, and TCF7L1). The Kaplan-Meier curve and the receiver operating characteristic curve (ROC, 1-year AUC: 0.723, 3-year AUC: 0.775, 5-year AUC: 0.786) showed that our model could be used to predict the prognosis of patients with COAD. Multivariate Cox analysis also reported that the risk model is an independent prognostic factor of COAD. The external cohort (GSE17536 and GSE39582) was used to validate our risk model, which indicated that our risk model may be a reliable predictive model for COAD patients. Finally, based on the model and the clinicopathological factors, we constructed a nomogram with a C-index of 0.802. In conclusion, we emphasize the clinical significance of TFs in COAD and construct a prognostic model of TFs, which could provide a novel and reliable model for the prognosis of COAD.

Project description:Osteosarcoma is a malignant tumor with a poor prognosis. Nowadays, there is a lack of good methods to assess the prognosis of osteosarcoma patients. Transcription co-factors (TcoFs) play crucial roles in transcriptional regulation through the interaction with transcription factors (TFs). Many studies have revealed that TcoFs are related to many diseases, especially cancer. However, few studies have been reported about prognostic prediction models of osteosarcoma by using TcoF-related genes. In order to construct a prognostic risk model with TcoF-related genes, the mRNA expression data and matched clinical information of osteosarcoma were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and the Gene Expression Omnibus (GEO) database. TARGET was used as a training set and GSE21257 from GEO was used as a validation set. Univariate Cox regression was performed to select 13 TcoF-related candidate genes, of which five genes (LMO2, MAML3, MTF2, RBPMS, and SIRT1) were finally used to construct the prognostic risk model by LASSO Cox regression analysis. The Kaplan-Meier (K-M) survival curves showed an obvious difference between high- and low-risk groups. The receiver operating characteristic (ROC) curves based on TARGET demonstrated that this risk model was credible (1-year AUC: 0.607; 3-years AUC: 0.713; 5-years AUC: 0.736). Meanwhile, the risk model was associated with immune cells and immune-related functions. By combining the risk score and clinical factors, the nomogram of osteosarcoma was assessed with a C-index of 0.738 to further support the reliability of this 5-gene prognostic risk model. Finally, the expression of TcoF-related genes was validated in different cell lines by quantitative real-time PCR (qRT-PCR) and also in different tissue samples by immunohistochemistry (IHC). In conclusion, the model can predict the prognosis of osteosarcoma patients and may provide novel targets for the treatment of osteosarcoma patients.

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Autophagy-related gene model as a novel risk factor for schizophrenia.

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Autophagy-related gene model as a novel risk factor for schizophrenia.

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